One of the most fascinating things about Real-Time Bidding (RTB) is that it takes only fraction of a second (100 milliseconds to be precise) for an ad space to be purchased. The user sees the ad and remains blissfully oblivious to the battle of bids that took place only few milliseconds before that.

In the Real-Time Bidding environment, the available ad space is subject to auction between competitive bids. And it is the job of the Demand-Side Platforms (DSPs), alongside with optimization technologies such as Skylads, to determine the optimal amount to bid for the desired impression. Since the buying process takes milliseconds to happen, that means that the players involved in the bid decision-making have extremely limited time to perform a complex data-driven assessment of the impression and place their offer.

The process also involves evaluation of all information provided by the advertiser, their concrete campaign objectives, the market historic data, and any other relevant information that might be useful (such as the one extracted from Data Management Platforms for example, or external data providers) and compare it all to the probability of the impression to result in a good conversion metric. This assessment is done programmatically with the help of Machine Learning algorithms.

Leveraging on the mathematical models within their structure, algorithms are able to swiftly examine this vast bulk of data and calculate the most appropriate price for the buying bid. They can look simultaneously at all the information and immediately pinpoint the key metrics that need to be taken into account.

As we all know, the burden of the decision-making lies in the uncertainty of its outcome. The ultimate strength of the algorithms is their ability to simultaneously evaluate between thousands of possibilities and chose the most ideal action that will lead to maximized performance. Mathematics is the art of finding the simple solution to a complex situation, and as such is playing a crucial role in the Real-Time Bidding process.